Pavement condition analysis from modeling impact of traffic characteristics, weather data and road conditions on segments of a transportation network infrastructure

ABSTRACT

A pavement condition analysis system and method models a state of a roadway by processing at least traffic and weather data to simulate the impact of traffic and weather conditions on a particular section of a transportation infrastructure. Traffic data is ingested from a plurality of different external sources to incorporate various approaches estimating traffic characteristics such as speed, flow, and incidents, into a road condition model to analyze traffic conditions on the roadway in order to improve road condition assessments and/or prediction. A road condition model applies these traffic characteristics, weather data, and other input data relevant to road conditions, accounting for heat and moisture exchanges between the road, the atmosphere, and pavement substrate(s) in a pavement&#39;s composition, as further influenced by traffic and road maintenance activities, to generate accurate and reliable simulations and predictions of pavement condition states for motorists, communication to vehicles, use by industry and public entities, and other end uses such as media distribution.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This patent application claims priority to U.S. provisional application61/863,446, filed on Aug. 8, 2013, the contents of which areincorporated in their entirety herein.

FIELD OF THE INVENTION

The present invention relates to pavement condition analysis.Specifically, the present invention relates to simulating and predictingpavement condition states in response to traffic characteristic data,weather data, and known road conditions.

BACKGROUND OF THE INVENTION

Predictions of pavement condition states on segments of a transportationinfrastructure of roadways such as a highways and bridges withsatisfactory lead times is a notoriously difficult undertaking. Forexample, frost development in a transportation infrastructure setting isvery hard to forecast due to large error magnitudes within the field ofmeteorology, whereas frost forecasting requires very accurate dataregarding dew point and pavement temperature, which are furtherdependent upon the material composition of the underlying pavement andsubstrates of a road, bridge, or other segment feature.

Use of meteorological data to generate pavement and/or bridge deckcondition predictions is also problematic, as there are many influencingfactors that are highly variable. Some of these factors are the albedo,heat capacity, conductance, texture, and emissivity of the pavement andits substrates, the solar and long wave radiation received at the topsurface of the pavement, shading effects by surrounding trees andterrain, the atmospheric temperature, humidity, wind speed, and thevarious forms of precipitation, as well as the profound effects ofwinter maintenance and treatment activities, and additionally,characteristics of traffic flow, patterns, and usage. There is nocurrently-available system or method that considers all of these factorsand accounts for their variances to produce a comprehensive model ofpavement condition behavior.

Further, there is no existing system or method that incorporates all oftraffic, weather, and known road conditions, either real-time orforecasted, to augment the simulation of a pavement's behavior so as togenerate a more realistic representation of what current conditions looklike and what future conditions will be. There is likewise no existingsystem or method for generating sophisticated output content for use bymotorists, for communication to vehicles for automatic settingadjustments, for private and public entities, or for media consumptionin response to such a pavement and road condition model, such as forexample visualized representations in the form of cross-sectionaltime-series animations of pavement conditions.

BRIEF SUMMARY OF THE INVENTION

It is therefore one objective of the present invention to provide asystem and method of generating predictions of pavement condition statesfrom a simulated pavement behavior, in which at least traffic andweather information is integrated to produce a more realisticrepresentation of current and future pavement conditions. It is anotherobjective of the present invention to improve the analysis of pavementcondition states generated as a result of simulating and predictingpavement condition states from pavement behavior by using sophisticatedapproaches to estimating the effects, and road condition implications,of traffic speed and flow, and weather data from a variety of sensorsand other resources. It is still another objective of the presentinvention to interpret these simulations and predictions of pavementcondition states into further output data for use by motorists,vehicles, companies, state and federal agencies, and media outlets.

The present invention discloses a system and method of modeling a stateof a roadway in a framework for pavement condition analysis thatincorporates at least traffic and weather data to simulate the impact oftraffic characteristics and weather conditions on a particular section,or segment, of a transportation infrastructure over specific periods oftime. Traffic data, weather data, and data regarding roadcharacteristics are ingested from multiple sources, including forexample data provided by third parties and/or collected from sensors,and may be pre-processed so that traffic and meteorological profiles aredeveloped to reflect estimated and/or forecasted traffic speedinformation and localized weather data. All of this input data is usedto model mass and energy balances in heat and moisture exchanges betweenthe road, the atmosphere, and a substrate(s) in a pavement'scomposition, and generate accurate and reliable simulations andpredictions of pavement condition states for motorists, communication tovehicles, use by industry and public entities, and other end uses suchas media distribution.

In one exemplary embodiment, the present invention discloses a method ofintegrating traffic, weather and road condition data for modelingpavement conditions in a transportation infrastructure network,comprising one or more of the elements of ingesting, as a first set ofinput data, weather data collected from one or more of weather sensors,satellite networks, vehicle-based systems, and numerical weatherprediction models, and developing a meteorological profile representingweather conditions on a roadway segment of a transportationinfrastructure for which pavement conditions are to be modeled;ingesting, as a second set of input data, traffic data, and developing atraffic profile representing traffic conditions on the roadway segmentfor which pavement conditions are to be modeled; ingesting, as a thirdset of input data, road condition data collected from one or more ofroad sensors, mobile sensors, and vehicle-based systems, and developingstandardized road condition reports representing roadway conditions onthe roadway segment for which pavement conditions are to be modeled;assimilating, in a computing environment comprised of hardware andsoftware components that include at least one processor configured toanalyze the meteorological profiles, the traffic profiles, and thestandardized road condition reports in a road condition model thatsimulates and iteratively adjusts pavement condition states frombehavior of a pavement response to one or more of 1) changes in statesof moisture resulting from heat and moisture exchanges between apavement surface, the atmosphere, and one or more pavement substrates ina pavement's composition 2) traffic flow characteristics that includedeviations from a normal traffic state at a specified period of timeindicated in the traffic profile, and 3) experienced roadway conditionsfrom the standardized road condition reports, and predicts pavementcondition states over the specified period of time; and generating oneor more of a plurality of output pavement conditions from the simulatedand predicted pavement condition states to output data modulesconfigured to develop spatial, trip, and time-series content in one ormore related application programming interfaces, and interpretations ofthe simulated and predicted pavement condition states for distributionto output data modules configured to develop enhanced commercial contentin one or more related application programming interfaces.

Another exemplary embodiment of the present invention discloses a systemcomprising one or more of the components of a computer processor and atleast one computer-readable storage medium operably coupled to thecomputer processor and having program instructions stored therein, thecomputer processor being operable to execute the program instructions tooperate a road condition model in a plurality of data processingmodules, the plurality of data processing modules including: a dataingest module configured to receive, as input data, weather datarepresenting meteorological conditions experienced on segments ofroadway a transportation infrastructure, and traffic data representingtraffic conditions experienced on segments of roadway in atransportation infrastructure, and road condition data representingfactors relating to experienced road conditions on segments of atransportation infrastructure; a pavement analysis module configured tosimulate and iteratively adjust pavement condition states from behaviorof a pavement in response to one or more of 1) changes in states ofmoisture resulting from heat and moisture exchanges between the roadsurface, the atmosphere, and one or more pavement substrates in apavement's composition, 2) traffic flow characteristics that includedeviations from a normal traffic state at a specified period of time,and 3) experienced road conditions, and a forecast module configured togenerate predictions of pavement condition states over the specifiedperiod of time.

In yet another exemplary embodiment, the present invention discloses amethod of modeling pavement conditions in a transportationinfrastructure network, comprising one or more of the elements ofdeveloping associations of input data to road segment metadata in aplurality of profiles in at least one processor in a computingenvironment comprised of hardware and software components to enable aplurality of data processing functions in a road condition model, theplurality of profiles include a meteorological profile representingweather conditions, a traffic profile representing traffic conditions,and standardized road condition reports representing experienced roadconditions, for roadway segments of a transportation infrastructure;simulating and iteratively adjusting pavement condition states from abehavior of a pavement for a roadway segment in response to one or moreof 1) changes in states of moisture resulting from heat and moistureexchanges between the road surface, the atmosphere, and one or morepavement substrates in the pavement's composition as a result of impactson the pavement by weather, traffic, and road conditions experienced onthe roadway segment, 2) traffic flow characteristics that includedeviations from a normal traffic state, at a specified period of time,and 3) the experienced road conditions from the standardized roadcondition reports; and predicting pavement condition states over thespecified period of time, wherein predictions of pavement conditionstates at least comprise forecasts of pavement surface temperatures andconditions.

Other objects, embodiments, features and advantages of the presentinvention will become apparent from the following description of theembodiments, taken together with the accompanying drawings, whichillustrate, by way of example, the principles of the invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments of theinvention and together with the description, serve to explain theprinciples of the invention.

FIG. 1 is a block diagram of types of input data ingested forassimilation of weather, traffic, and road condition information in aframework of pavement condition simulation and prediction according tothe systems and methods of the present invention;

FIG. 2 is a block diagram of a data assimilation process in a frameworkof pavement condition simulation and prediction according to the systemsand methods of the present invention; and

FIG. 3 is a block diagram of prediction and output dissemination in aframework of pavement condition simulation and prediction according tothe systems and methods of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following description of the present invention reference is madeto the exemplary embodiments illustrating the principles of the presentinvention and how it is practiced. Other embodiments will be utilized topractice the present invention and structural and functional changeswill be made thereto without departing from the scope of the presentinvention.

The present invention is a system and method of simulating andpredicting pavement conditions states from behavior of a pavement, in aframework for pavement condition analysis. The present inventionincorporates various paradigms for estimating traffic characteristicssuch as speed, flow, and incidents, together with other inputs such asweather and data concerning road condition characteristics relative toroadway performance, into a road condition model to analyze pavementconditions by simulating pavement condition states from behavior of apavement in response to traffic, weather, and road conditions on aparticular section or segment of a transportation infrastructure, andprovide predictions of pavement condition states over specific periodsof time.

The systems and methods described herein may also ingest input data thatis generated from traffic estimation and/or prediction models and mayalso incorporate real-time and historical traffic information eitherdirectly or indirectly, such as for example from various sensors. Thepresent invention may further incorporate additional input data foringest into the road condition model for simulating the impact oftraffic characteristics, such as for example weather sensor information,data from radar and video components, real-time traffic and weatherobservations provided from a number of different sources such ascrowd-sourced information provided via social media feeds, historicalweather data, and information from any other source which can beutilized by the road condition model to formulate analyses andpredictions of pavement conditions as described further herein.

FIG. 1 is a block diagram of types of input data ingested forassimilation of weather, traffic, and road condition characteristics ina pavement condition analysis framework 100 according to the systems andmethods of the present invention. The various types of input datadescribed herein are ingested into weather conditions input system 110,a traffic conditions input system 120, and a road conditions inputsystem 130. In the pavement condition analysis framework 100, input datafrom these systems are provided to a road condition model assimilationmodule 140 for further processing in a road condition model 190.

The road condition model 190 of the pavement condition analysisframework 100 predicts pavement condition states by analyzing andmodeling both mass and energy fluxes and balances in simulated pavementbehavior in response to the various types of input data, using anequation of unsteady heat flow, combined with sophisticatedparameterizations for representing heat and moisture exchanges betweenthe road, the atmosphere, and the pavement composition, such as one ormore substrates. Balance between mass and energy, particularly in apavement condition context, means that changes in the state of moistureoccur only as energy flows permit, so that for example, evaporatingmoisture away from the road surface requires energy from the roadsurface, which cools it. Dew or frost formation have the opposite effectof putting energy into the pavement. Perhaps more important, however,are changes between liquid and solid states of moisture. For example, inorder for a road that has ice on it to warm above freezing, orvice-versa, the latent heat of fusion must be overcome. This normallycauses the road temperature to stabilize at the freeze point temperaturewhile this phase transition occurs. This also means that when moisture(as snow, rain, frost, dew) is deposited onto the road it also transfersenergy to or from the road, and that evaporation or sublimation ofmoisture from the road requires the road to have an adequate amount ofenergy available to support those processes.

Such energy transfers have a profound effect on roadway conditions andon travel thereupon. The present invention applies a plurality of inputdata from different sources as discussed herein to improve upon themodeling of these mass and energy balance distinctions to generate morereliable and accurate analyses, simulations, and predictions of pavementconditions, and consequently, to generate information for travelersusing affected roadways as well as third parties, such as maintenanceproviders, state or federal agencies tasked with transportationinfrastructure management, traveler information providers, and mediaoutlets.

One methodology for capitalizing on distinctions between mass and energybalance in the present invention is by using the fact that the freezepoint of water can be reduced by adding certain chemicals to a treatmentmixture to be applied to a pavement, such as for example salt. The roadcondition model 190 partitions the moisture atop the pavement surfaceinto sections representing different possible forms that moisture cantake (e.g., liquid, snow, ice, frost, compacted snow, etc.), and thenuses the eutectic properties of any chemicals that are added to the mixto repartition the moisture between these sections. In thisrepartitioning process, mass and energy balance are maintained, sincewhen salt is applied to a pavement with frozen moisture on it, thecomposition and pavement surface temperature will typically undergo arapid drop, followed by a slower recovery. This occurs because theenergy required to melt the ice is coming from the pavement, and all thesalt has done is change the temperature where equilibrium exists (i.e.,where there is no tendency for energy to flow from the pavement to theice, or vice-versa).

As time passes, energy will normally be drawn upward from lower in theroadbed either in or beneath the pavement substrate, permitting the roadto warm back up to near its original temperature again. This permits theroad condition model 190 to simulate the simultaneous impacts ofmultiple deicers, each with differing properties. The mixing ofchemicals requires an iterative approach to finding where theequilibrium state lies.

The importance of this ability to appropriately manage the partitioningof moisture into its different forms is that it directly influences howtraffic will impact the pavement's condition. With sufficient liquidmoisture present, traffic acts to splatter or spray the entire mixtureoff of the pavement surface. As the amount of liquid in the mixturedecreases, transitions in this behavior occur, first to a consistencywhere the moisture atop the pavement is simply moved short lateraldistances with the passage of each successive vehicle, and eventually toa consistency where the mixture is increasingly taken under the tires ofeach successive vehicle where it can be compacted into a more hardenedform that is both difficult to travel upon and difficult to remove.Winter maintenance activities often seek to maintain sufficient liquidin this mixture so as to prevent this deterioration.

The weather conditions input system 110 facilitates ingest of manydifferent types of weather-related data into the road condition modelassimilation module 140 of the pavement condition analysis framework100. This includes data related to meteorological characteristics suchas atmospheric temperature, humidity, wind speed, various forms ofprecipitation, downwelling radiation, and other such information, andmay be derived from, for example, radar data 110 collected from radarsensing systems, and satellite data 112 from satellite-based weatherobservation and tracking systems. The weather conditions input system110 also ingests data from numerical weather prediction models 113 andsurface networks 114 such as described further herein. Weather data mayalso be derived from data generated from crowd-sourced observations 115using mobile applications configured on devices such as telephones andtablets, and using social media feeds. Weather data may also begenerated from meteorologist input 116.

Other types of data ingested into the weather condition input system 110include image-based data 117 from systems such as video cameras, andvehicular data 118 generated from one or more vehicle-based sensingsystems, including those systems coupled to vehicle computing systems,or those systems configured to gather weather data from mobile devicespresent within vehicles, such as the mobile telephony devices and tabletcomputers noted above. Together with the traffic-related and roadcondition information as discussed further herein, the different sourcesof weather or environmental data contemplated by the present inventionfurther enhance reliability and accuracy of the simulations of pavementbehavior performed by the road condition model 190, as well assubsequent forecasts of states of pavement conditions.

The different sources of weather data ingested into the weatherconditions input system 110 may include data from both in-situ andremotely-sensed observation platforms. For example, the numericalweather models (NWP) 113 and/or surface networks 114 may be combinedwith data from weather radars and satellites to reconstruct the currentweather conditions on any particular link or segment of roadway. Thereare numerous industry NWP models available, and any such models may beused to input weather variables in the present invention. NWP modelsused herein at least include RUC (Rapid Update Cycle), WRF (WeatherResearch and Forecasting Model), GFS (Global Forecast System), and GEM(Global Environmental Model). This weather data is received inreal-time, and may come from several different NWP sources, such as fromMeteorological Services of Canada's (MSC) Canadian Meteorological Centre(CMC), as well as the National Oceanic and Atmospheric Administration's(NOAA) Environmental Modeling Center (EMC), and many others.Additionally, internally or privately-generated “mesoscale” NWP modelsdeveloped from data collected from real-time feeds to global observationresources may also be utilized. Such mesoscale numerical weatherprediction models may be specialized in forecasting weather with morelocal detail than the models operated at government centers, andtherefore contain smaller-scale data collections than other NWP modelsused. These mesoscale models are very useful in characterizing howweather conditions may vary over small distances and over smallincrements of time. The present invention may be configured to ingestdata from all types of NWP models into the weather conditions inputsystem 110, regardless of whether publicly, privately, or internallyprovided or developed.

The traffic conditions input system 120 facilitates ingest of manydifferent types of traffic-related data into the road condition modelassimilation module 140 of the pavement condition analysis framework100. This includes traffic speed data 121, traffic volume data 122, androadway incident data 123 reflective of real-time and/or actualconditions being experienced on a roadway. Such traffic-related data maybe ingested, for example, as an output of a traffic state estimationmodel from a traffic estimation platform that generates analyzed trafficcharacteristics such as speed and volume information from one or moresensors or third party sources. The traffic conditions input system 120may also ingest predicted traffic speed data 124 and predicted trafficvolume data 125 from a traffic prediction platform. Crowd-sourcedobservational data 126 may also be provided from individuals usingmobile telephony devices or tablet computers using software tools suchas mobile applications. This analyzed and predicted traffic-related datamay be realized from many different sources as noted further herein.Depending on the source, data may be provided in either a raw form or aprocessed form. Processed data may be subject to a variety of paradigmsthat take data generated by sensors or partners and extract relevanttraffic information from a traffic estimation platform, for analyzedtraffic characteristics, and from a traffic prediction platform forpredicted traffic characteristics as noted above, for subsequent use inthe road condition model 190 of the present invention.

One such source is from external partners that collect probe datagenerated by global positioning system (GPS) devices. As noted above,this GPS probe data may be either in a raw form or in a processed form.Raw probe data is a collection of bulk data points in a GPS dataset,while probe data that has been processed has already been associatedwith information such as traffic speed on a roadway network. Prior toingest as input data into the road condition model 190, this GPS probedata may be initially processed to develop speed estimates acrosstraffic networks representing large geographic areas. Each such networkis comprised of inter-connected links, but it is often the case thatobtaining complete link speed estimates is hindered by the sparseness ofthe input data—i.e., GPS data is typically available for only part ofthe links representing a larger transportation network, and only forpart of the time. In other words, collected GPS data is incomplete,making it hard for these existing systems to accurately estimate trafficspeed across inter-connected network segments. Additionally, the qualityand comprehensiveness of GPS probe data varies by vendor. One or moreprocessing techniques may be used in a traffic state estimation model toiteratively smooth out this data prior to ingest into the road conditionmodel 190, so that any missing values are temporally and spatiallyfilled in to ensure accuracy in the traffic information derivedtherefrom.

These processing techniques iteratively smooth out this data byidentifying missing speed values in the dataset. This is done byapplying a procedure to map known GPS data to road links, in a processknown as snapping. It then determines neighboring links in the same linknetwork using network distance and road distance limits on the linkvalues. This is followed by steps in which the present invention usesinitial data in the GPS data set to build a rescaled speed profile aswell as a free-flowing speed estimate. The speed profile could becompressed via a clustering analysis to reduce storage requirements. Theresult is a model that can be applied in real-time to fill in themissing values in an input data set by applying the snapping procedureto the GPS data, and then applying a temporal and spatial smoothingprocedure to the known speed data using the rescaled speed data toarrive at sufficient estimates for the missing values. In cases wherethere is even less data, the profile based method is used to infermissing values. Once this is accomplished, an accurate traffic speed canbe estimated from the incomplete GPS speed data. In other words, thepresent invention utilizes observed information for one link to estimateneighboring links that are missing observed information, and appliesthis process to provide a traffic speed estimate for all links at alltimes.

Another external source of traffic information may be provided byBluetooth field units configured to read signals emitted by otherBluetooth devices. These field units, or readers, are passive deviceswhich may be positioned at or near sections of a roadway to be modeledto collect vehicular information in the vicinity of that segment. Thesereaders are configured to communicate with devices equipped withBluetooth capabilities used by motorists in vehicles, such as mobiletelephones and computers. Such devices are often used in situationswhere hands-free communications are either advantageous or mandatory,for example while driving. Because Bluetooth devices use a radiocommunications system, readers and transmitters do not have to be invisual line of sight of each other—instead, only a quasi-opticalwireless path must be present to enable a Bluetooth reader, positionedat or near the side of a roadway, to detect a signal emitted by aBluetooth device in a vehicle.

As with the GPS probe data, one or more processing techniques may beapplied prior to ingest into the road condition model 190 to determinetraffic information such as vehicular speed, position, proximity andincidents from data collected by Bluetooth readers. In one embodiment, aReceived Signal Strength Indicator (RSSI) from one or more signalsemitted by the Bluetooth devices may be observed to analyze vehicularinformation from a signal strength emitted from motorists passing one ormore of such readers. RSSI measurements provide the ability to determinea relative distance of each Bluetooth-enabled mobile device from thereader by analyzing the strength of a signal observed, or detected, byeach reader. These measurements are applied to the one or more dataprocessing modules which may employ a mathematical approach usingFourier Transforms to determine peak signal strength from RSSI data andpinpoint vehicular sample location closest to a Bluetooth reader. Oncedetermined, this sample location is then used for several purposes.First, this sample location can be used to derive a vehicle's positioninformation, which may then be used to generate more accurate traveltimes. Second, this positional information can be combined with approachread data to create an approach vector, which may then be used todetermine approximate vehicle speed. Finally, multiple approach vectorscan be combined to detect roadway incidents. Bluetooth field units aretherefore used to perform spot analytics by mathematically modeling thesignal strength to extract data relevant to traffic information in andaround a roadway link to be analyzed for pavement condition modeling inthe present invention, such as vehicular position, vehicular speed,proximity of vehicles to each other, and traffic incidents.

In another embodiment, Bluetooth field units may be configured todetermine traffic information from additional information emitted bypassing Bluetooth devices. The present invention uses these field unitsto detect MAC addresses from Bluetooth transmitting devices in thevicinity of the unit. These MAC addresses are logged together with thetime of detection, so that counts of all passing vehicles enable thenumber of vehicles passing the readers during any period to becalculated. A comparison of logs from multiple readers allows the traveltime between points to be calculated. The vehicle counts also enableestimates of the total delay between two data collection points (thereaders). Data from a plurality of readers may further enable thecalculation of one or more paths of vehicles from vectors representingorigin and destination points, for an analysis of how real-time trafficis maneuvering in and around roadway links for which pavement is to bemodeled.

The road conditions input system 130 facilitates ingest of dataconcerning many different types of road condition characteristics intothe road condition model assimilation module 140 of the pavementcondition analysis and prediction framework 100. Such data relatesgenerally to other characteristics of roadway performance, such as forexample maintenance activities and effects of maintenance treatmentsapplied to surfaces in adverse conditions. This includes road sensordata 131, which incorporates information from sensors placed in or nearroadway surfaces to detect and monitor pavement conditions. It alsoincludes data collected by mobile sensors 132 and vehicular data 133.Other types of data ingested into the road conditions input system 130include reports 134 from states or other entities responsible forroadway performance or maintenance, work zone data 135, snowplow data136, and image-based data 137 such as that collected by systems such asvideo cameras. Reports 134 may be generated by, for example, statedepartments of transportation, from state highway patrol departments,and any other such organization responsible for transportation networks.Crowd-sourced observational data 138 may also be provided fromindividuals using mobile telephony devices or tablet computers usingsoftware tools such as mobile applications, and from other sources suchas social media feeds.

In one embodiment of the present invention, this type of road conditiondata is generated by one or more maintenance decision support systemswhich ingest such data into the road conditions input system 130.Maintenance decision support systems generally are systems operated ormaintained by agencies and entities responsible for wintertransportation infrastructure maintenance collect data from weathermaintenance vehicles, often using mobile/maintenance data collection andautomated vehicle location systems (known in the industry together asMDC/AVL systems). Such systems utilize global positioning systems (GPS)and on-board data logging and/or transmission capabilities to provideinstantaneous GPS-tagged reports of winter maintenance vehicleactivities (e.g., plow position(s), material applications, etc.) and/orobserved environmental conditions (e.g., road temperatures and/orconditions, etc.). Road condition information from these types ofsystems further enhances reliability and accuracy of the simulationsperformed by the road condition model 190, as well as subsequentpredictions of pavement condition states, by incorporating dataregarding the impact of weather and maintenance activities on thepavement and its various material compositions.

Road condition data may be provided by maintenance decision supportsystems that are configured to generate information on the impact ofweather and maintenance activities using a variety of formats, and fromdifferent configurations of such systems themselves. Accordingly, it iscontemplated that where data is ingested into the road conditions inputsystem 130 and the road condition model assimilation module 140, it isnot to be limited to any one or specific format or configuration. Forexample, in one embodiment, road condition data may be ingested fromsystems configured to quantify maintenance activities that beingperformed, and the results of those activities, and also configured forindependently simulating maintenance activities which were required, andthe expected results of those activities, in response to observedweather conditions. In other words, the present invention contemplatesthat road condition data may be ingested into the road condition model190 in a pre-processed form that both enables winter maintenancemanagers to better understand their current winter maintenanceoperations, and provides independent, weather-sensitive metrics againstwhich the effectiveness and efficiency of winter maintenance operationscan be evaluated, prior to additional simulations performed usingweather and traffic information.

It is to be further contemplated that where such maintenance decisionsupport systems are utilized to provide road condition data, the presentinvention is not to be limited to any software or hardware configurationof such a system. For example, processing of data performed by amaintenance decision support system may be either centralized orlocalized. In one embodiment, therefore, road condition data may beprovided by maintenance decision support systems in which users retrieveand manipulate information needed to perform the various actionsattendant to roadway maintenance decision-making at a localized level.Such localized systems may be software-based applications that utilize aplurality of modules within a hardware and software computingenvironment configured to perform customized modeling of road conditionsin response to the data ingested, such as weather, observed roadconditions, and data from sensors and instruments, and to generatetreatment recommendations for winter maintenance activities. Modeling ofroad condition data in such a framework is performed at or near the areaof the roadway where maintenance treatments may be applied.

FIG. 2 is a block diagram of a process of data assimilation in thepavement condition analysis framework 100. The weather conditions inputsystem 110, traffic conditions input system 120, and road conditionsinput system 130 are each communicatively coupled to a road networkdatabase 150, so that metadata and other information relative tosegments, or links, or a roadway network are correlated for the variousweather, traffic, and road condition inputs to which they apply.

Using the information from the road network database 150, the weatherconditions input system 110 aggregates the incoming weather input dataand generates meteorological profiles 160 for road segments beingmodeled. The traffic conditions input system 120, using information fromthe road network database 150, aggregates the incoming traffic data andgenerates road segment traffic profiles 170 comprising speed and volume,or reductions, for a particular road segment. Additionally, the roadconditions input system 130, also using the information from the roadnetwork database 150, aggregates the incoming road condition input dataand generates standardized road condition reports 180. The road segmentmeteorological profiles 160, road segment traffic profiles 170comprising speed and volume reduction information for a particular roadsegment, and the standardized road condition reports 180 associate theweather data, traffic data, and road condition data with segments of theroadway network to which they relate, and are communicated to the roadcondition model assimilation module 140 for further assimilation ofinput data for the road condition model 190 and simulation of responsesto the conditions identified in the various types of input data.

The ingested data described above, and the association of ingested datato develop road segment meteorological profiles 160, road segmenttraffic profiles 170, and standardized road condition reports 180 fromsuch data, occurs within a computing environment forming at least a partof the pavement condition analysis framework 100. The computingenvironment includes software and hardware components configured toexecute program instructions in one or more data processing modules toperform the simulations of pavement conditions, and subsequent pavementcondition predictions, as described further herein. Also included is adata ingest module configured to receive the input data from manydifferent sources, also as further described herein.

The road condition model 190 adjusts the simulation process withnumerical, time-step integration of input data 110 so that pavementcondition states based on the behavior of a pavement are iterativelygenerated. Accordingly, the pavement condition analysis framework 100models input data 110 in an ongoing process in which adjustments areiteratively made during the simulation process. These iterativeadjustments are performed in response to one or more of changes instates of moisture resulting from heat and moisture exchanges between apavement surface, the atmosphere, and one or more pavement substrates ina pavement's composition indicated in a meteorological profile, trafficflow characteristics that include deviations from a normal traffic stateat a specified period of time indicated in a traffic profile, andexperienced roadway conditions indicated in standardized road conditionreports.

At least it pertains to such experienced roadway conditions, theiterative aspect of the simulation and adjustment may be performed for aspecified period of time, such as to account for deviations between asimulation outcome and an experience roadway condition. For example, theroad condition model 190 may produce a simulated pavement state that isjust above freezing for a given time. However, if a road conditionreport is received that indicates an icy roadway condition at that time,the road condition model 190 can switch the form of the moisture on thepavement to ice. When this occurs, the road condition model 190, stillhaving pavement temperatures above 32° F., starts melting some or all ofthis ice in subsequent numerical integration time-steps. The ‘icy’ roadcondition report is therefore re-applied again and again in iterativeadjustments, after each numerical integration time-step, for a specifiedperiod of time (typically on the order of 15-30 minutes, which might beon the order of 10-20 numerical integration time-steps). Thereintroduction of the ice to the pavement surface after each time-stepcontinually takes heat away from the pavement in each time-step, untilafter some number of time-steps the pavement in the road condition model190 is cold enough to maintain that icy condition. At that point theroad condition model 190 stops iteratively re-applying the icyobservation from a road condition report. Therefore, such experiencedroad conditions are imposed on the road condition model 190 for only along enough period of time to permit the related aspects of the pavementtemperature to come into equilibrium with the experienced roadcondition.

In addition to the sources of input information discussed above, theroad condition model 190 is also communicatively coupled with one ormore additional database units to obtain additional data to facilitatethis simulation and iterative adjustment of pavement condition statesfrom roadways' responses, and subsequent prediction of pavementconditions within the pavement condition analysis framework 100. Theadditional database units include a road segment characteristicsdatabase 200, a database of road condition model processingconfigurations 210, and a database of management agency policies andpractices 220.

Road segment characteristics database 200 is accessed by the roadcondition model 190 to obtain additional link information about thesegments of roadway being simulated. For example, the road conditionmodel 190 may require geo-spatial coordinates for accurate and reliablesimulations of pavement behavior in response to the various types ofinput data, as well as specific features and other characteristics ofthe segment of roadway. Examples of such features and othercharacteristics include the specific material composition of thepavement and its substrate(s), the age of the segment, whether thesegment is or includes bridges and the type of bridge(s) present, andthe elevation and/or slope of the segment. Many other such features andcharacteristics may be possible, and road condition model 190 isconfigured to access the road segment characteristics database 200 orany other such database for this information.

The road condition model processing configurations database 210 isaccessed by the road condition model 190 to obtain information andinstructions for the specific processing paradigms to be performed whensimulating pavement behavior in response to the various types of inputdata. For example, as noted below, one way in which the road conditionmodel 190 simulates the impact of traffic on pavement conditions is byapplying “virtual” vehicles at a rate consistent with reality for agiven link. The road condition model processing configurations database210 stores characteristics relevant to such a “virtual” vehicle, such astire properties, width between tires, specific track it follows withinlanes, and various parameters describing the impact of each tire onvaried moisture compositions at a range of travel speeds. Other examplesinclude information regarding the physical, thermal and radiativeproperties of the pavement and its substrate in order to simulate energycapacities and flows within the pavement profile; information regardingeutectic and other properties of deicers and abrasives that may be usedon a roadway section or segment; and information relating properties ofthe ambient environment to expected roadway impacts.

The maintaining agency policies and practices database 220 is accessedby the road condition model 190 to obtain information used to modulatethe simulations and resultant forecasts generated by the presentinvention relative to how the roadway is maintained by the agencies orentities responsible for infrastructure maintenance. For example,accurate and reliable simulations performed by the road condition model190 may be improved by incorporating policies and practices formaintaining pavements in various weather, traffic, and roadwaymaintenance patterns. The road condition model 190 may therefore be ableto improve simulations and predictions by modeling pavement response tocertain treatment materials applied to the roadway. Since differentagencies and entities have different policies and practices, simulationsfor different segments of a transportation infrastructure may vary basedon its location, type, and characteristics, which in turn may relate toresponsibility for its maintenance. Other examples include the expectedhours of operation observed by the maintaining agency, and level ofservice specifications that describe road condition expectations duringand after a weather event. The maintaining agency policies and practicesdatabase 220 stores such information, and the road condition model 190is configured to query this database as part of the simulation andforecasting process as needed.

As noted above, one way in which the road condition model 190 simulatesthe impact of traffic on pavement conditions is by applying “virtual”vehicles at a rate consistent with reality for a given link, or roadwaysegment. Each such vehicle is assigned slightly differentcharacteristics, such as in terms of tire properties (width, number,etc.), width between tires, and the specific track it follows within alane on the roadway. As virtual traffic flows in the road conditionmodel 190, the various sections of moisture described above aretransformed as the moisture migrates across and/or off of across-section of the pavement, depending in part on the properties ofthat moisture. For instance, snow may be compacted under the tires ifinsufficient liquid is present, spread or splattered to the side if itis slushy, or suctioned off by the vacuum associated with the passingvehicle if it is light, fluffy and/or dry snow. Liquid water may besprayed or splattered away, or run off through the effects of gravity ona sloped road, etc. The end result is a simulation of the state of thepavement's surface and other layers that provides a realistic visualrepresentation of what the roadway is likely to look like to personstraveling the road, and hence the roadway conditions they are likely toexperience. This permits generation of realistic cross-sectionalanimations of pavement conditions as noted further herein, which aredirectly visually comparable to actual conditions, thereby aiding bothassimilation of visual reports of road conditions as well as conveyanceof expected road conditions to users of the simulated data.

The integration of traffic information, and the resulting road segmenttraffic profile 170 comprised of road segment speed and volume, orreductions, developed by the traffic conditions input system 120,regardless of the source and type of data contemplated herein, has theability to enhance the reliability and accuracy of road conditionassessments and/or prediction. Transportation agencies widely recognizethe impact of adverse road conditions on traffic flow, and it is oftenthe situation that such agencies use traffic data as an indicator oftheir winter maintenance performance during adverse weather. Thistraffic information influences the vehicular characteristics that arerated consistent with reality for a given link in the “virtual” vehiclescreated for modeling pavement behavior and condition states, at leastwith respect to the movement of the various sections of moisturedescribed above off of a cross section of the pavement, as virtualtraffic flows in the road condition model 190. In addition to this, thepresent invention also contemplates that traffic information such asspeed data may act as a surrogate for road condition observations, inparticular as a way of knowing whether the road is in good drivingcondition or not. The present invention is therefore configured toincorporate traffic information such as speed data, and other vehiculardata such as flow, proximity, and incidents, to adjust a runningassessment of the road conditions on each particular link stretch ofroad accordingly.

As noted above, the road condition model 190 also has the ability toassimilate observations 115, 126, and 138 of road conditioncharacteristics as input data for the data ingest module and for furtherenhancement of the traffic 170, meteorological 180 and road condition190 profiles developed. Such observations 115/126/138 may serve toaugment the amounts of moisture in the various sections partitioned bythe road condition model 190 as described above, as well as thecross-sectional distribution of that moisture, in a manner that isconsistent with the observation that is provided. Incorporation of suchobservations 115/126/138 has the effect of permitting the road conditionmodel 190 to recognize that there may be various places along thesegment being modeled where uncertainty exists. For instance, if anobservation that a segment of the road is wet is ingested as an input,yet the road condition model 190 has already considered that thissegment is covered in snow, it is quite possible that a chemicalapplication has occurred that the present invention was not previouslyaware of. In this case, it may be preferable to artificially add somechemical to the pavement surface to support the wet road conditionrather than adjusting the road condition to wet (which would likelysimply lead to the model refreezing it right away) or adjusting the roadtemperature upward. In this manner, the addition of road conditionobservations 115/126/138 modulate the road condition model 190 byadjusting the simulations performed to ensure that real-time traffic,weather, and roadway conditions are accounted for to generate the mostreliable and accurate output analysis of the pavement state.

There are many examples of different types of sources from which suchobservations 115/126/138 may be provided. For instance, motoriststhemselves may provide observations, such as in the form of textualinput into, or visual images collected by, mobile devices such astelephones and tablets used inside vehicles themselves. Observations115/126/138 may also be generated by maintenance workers or entitiestasked with applying treatments or performing maintenance activities,such as in roadway work zones. Still other observations may be providedby law enforcement personnel, in the vicinity of a roadway link to bemodeled. The present invention contemplates that any source of real-timeor current observations may be utilized, and that many different meansmay be used to enter and record such observations 115/126/138.

The plurality of different types of traffic data may further includesensor systems configured to collect information from either the roadwayitself, locations proximate to or near the roadway for which pavementconditions are to be modeled, or from vehicles passing through or nearthat particular section of the road. For example, data may be collectedfrom sensors embedded in sections of a roadway which are within thevicinity of the section being modeled, or the section to be modeleditself, such as with traditional loop detectors or road surfacecondition sensors. Data may be collected from video cameras oriented soas to capture images of a roadway, and from radar units configured todetect signals emanated onto a roadway and reflected back from vehiclesor road conditions that are present. Additionally, data may also becollected from vehicles themselves, such as for example from on-boardGPS devices or other sensors, or from mobile devices used by motorists.

Using the example of data collected from video cameras, the roadcondition model 190 may be configured to adjust its simulation ofpavement conditions based on image analysis of data ingested fromroad-oriented cameras. Such data may be able to provide visualcharacteristics of the pavement condition state which can be used toconfirm, modulate, or revise the simulated pavement compositioncondition, including the cross-sectional distribution of the differentforms of moisture that comprise this condition, so that image data mayat least be used as a method of adjusting the pavement conditionsimulations and predictions based on other data, such as for exampletraffic, weather, and maintenance or construction information.

FIG. 3 is a block diagram of components and processes for outputdissemination in a pavement condition analysis framework 100 accordingto the systems and methods of the present invention. As discussed above,the road condition model 190 analyzes the various types of input dataingested into the pavement condition analysis framework 100 byperforming simulations of pavement conditions at segments of theroadway. These simulations analyze distinctions between mass and energybalances using an equation of unsteady heat flow, combined withsophisticated parameterizations for representing heat and moistureexchanges between the road, the atmosphere, and pavement substrate andother components of its material composition, and produce simulations ofpavement condition states for segments simulated over specified periodsof time. Several different types of output data are generated as aresult of the data processing performed by the road condition model 190.

The road condition model 190 performs both the simulations andpredictions of pavement condition states using the input data ingestedvia the weather condition input system 110, the traffic conditions inputsystem 120, and the road conditions input system 130. Simulation outputsmay be generated as pavement condition analyses adjusted by the roadcondition model 190 using the information from the road segmentcharacteristics database 200, the road condition model processingconfigurations database 210, and the maintaining agency policies andpractices database 220. The road condition model 190 communicates thesesimulations to an adjusted analyses database 230, and applies thesesimulations to generate forecasts of pavement condition states, whichare communicated to a road condition predictions database 240.

The road condition model 190 is therefore configured to simulatepavement condition states from behavior of a pavement in response to thevarious input data and modulated by the additional information as notedabove, and to apply those simulations to generate predictions ofpavement condition states for the road segments simulated over specifiedperiods of times. These forecasts are then applied and/or interpretedfor output to applications for a plurality of commercial uses.

As noted above, the road condition model 190 performs these tasks in oneor more data processing modules that carry out the simulations ofpavement condition states from behavior of a pavement, and resultantpavement condition predictions. Accordingly, the road condition model190 may include a simulation portion configured to ingest the varioustypes of data via data ingest module and perform the modeling describedherein, and a prediction portion configured to access simulations fromthe adjusted analyses database 230 to construct predictions of pavementcondition states for specific road segments over specified periods oftime, and generate output paradigms for the plurality of commercialuses.

The adjusted analyses database 230 may be configured to maintaininformation related to traffic flow characteristics for specificsegments of a transportation infrastructure for various period of time.These traffic flow characteristics can be used by the road conditionmodel 190 to form “snapshots” of normal traffic states and expecteddeviations for specified periods of time. The road condition model 190can apply these normal traffic states and expected deviations tomodulate simulated behavior of the pavement in response to other inputdata and adjust output for consistency with traffic flowcharacteristics.

Pavement condition simulations and predictions are communicated from theroad condition analyses 230 and predictions 240 databases to a pluralityof modules 250 configured to generate application program interfaces(APIs) and visualizations of information derived from the analyses ofpavement condition states. One such data processing module is a spatialdata module 252 that generates spatial data and visualizations of suchspatial data, such as for example vectors of roadway conditions data andmap tiles, in one or more APIs configured to provide or support mappingfunctions for users. These APIs provide users with analyzed pavementcondition states in a map-based display of output data, or data objectsappropriate for representing spatial data, from the road condition model190. The map-based displays show pavement condition states in avectorized form, and the data displayed may be provided as tiles forrendering on a graphical user interface. The map-based displays may berendered in a variety of different ways, such as using animations, inthree dimensions, or any other known format.

Another of the modules 250 is a trip data API module 254 that generatestrip data and visualizations of trip data in one or more trip data APIs.For example, this module 254 generates forecasts of conditions for a setof locations and times defining a trip to be taken by users of the tripdata API. Similar to the spatial data APIs, the trip data APIs may beconfigured to provide trip data in appropriate data objects, or on agraphical user interface in a variety of different ways, such as usinganimations, in three dimensions, or any other known format.

Still another of the modules 250 is a time-series data API module 256that generates time-series representations and related visualizations ofpavement condition states in one or more time-series data APIs. Forexample, this time-series data API module 256 generates displays ofconditions over periods of time for segments of a roadway defined byusers in one more APIs configured to generate such time-seriesrepresentations. These APIs may be configured to generate thesetime-series representations in a number of ways, such as for exampledata objects, or graphical plots or animations on a graphical userinterface.

The pavement condition analysis framework 100 may further include aninterpretation module 260 configured to generate interpreted output datafrom the road condition model 190 for additional consumption in one ormore uses or products. For example, a module configured to facilitatetraffic model input APIs 262 packages data representative of pavementcondition states from the road condition model 190 and provides thatpackaged data to traffic flow models for improvements to trafficforecasting. Another example of additional consumption of interpretedoutput data in one or more uses or products is a module configured tofacilitate mobility APIs 264 and visualizations of related data.Mobility APIs 264 perform quantitative metrics on simulated pavementcondition states to assess difficulty of travel conditions along a routeor in an area.

Yet another module in FIG. 3 is an area summary API module 266. Thismodule 266 is configured to provide verbal and/or textual narrativesummarizations of road conditions along a route or in an area.Similarly, a trip summary API module 268 is configured to facilitateconversion of pavement predictions from the road condition model 190into verbal and/or textual narrative summarizations of road conditionsfor a user-defined trip along a route or in an area. The summarizationsgenerated by area summary API module 266 and trip summary API module 268may be delivered to users via either a graphical or aural user interfaceon a computer, tablet, or mobile telephony device, or via textmessaging, social media feeds, or any other form of deliveringtext-based narratives.

As noted above, the road condition model 190 analyzes behavior ofpavement responses using the input data provided, and generatessimulations and predictions of pavement condition states for specificsegments of a roadway for specified periods of time. These simulationsand predictions can be configured for a variety of downstream outputuses using the plurality of modules for APIs described above, such as tocommunicate information to vehicles and vehicle operators, for exampleby generating informational displays or messages to vehicles and theiroperators, or users of applications in vehicles, to trigger automaticadjustments to vehicle settings, and to create a visual representationof what the road is likely to look like to motorists traveling themodeled section of the road.

There are many different embodiments in which such simulations andpredictions can be used to generate further output data for consumer andother use. Examples of uses of such output data include adjustingvehicle settings, such as enabling traction control, disabling cruisecontrol, or other settings associated with automatic vehicle operation,communicating current and/or forecast road temperature or conditions,and communicating information about the risk of frost or other adverseconditions to drivers. In one embodiment, the present invention may beconfigured to utilize pavement surface condition simulations and/orforecasts to provide more useful information to a driver than ispresently available, such as when a vehicle is started, or when thevehicle is entering an area for which alerts would be helpful. Thepresent invention may include a data processing module that is capableof predicting the probability of frost, by comparing the proximity ofthe pavement and dew point temperatures, as well as the proximity of thepavement temperature to freezing, relative to the uncertainty in each.Where the road condition model 190 is able to make an assessmentpavement surface temperatures, it can use what is known about theuncertainty in those to assign a risk that frost is forming or willform, and deliver an informative message to a vehicle operator, such asfor example to reduce speed or change lanes. Such a message may becoupled with a touchscreen option, for example, to turn on vehiculartraction control. Information derived in this manner may be used todeliver content into a moving vehicle to assist motorists to reduce therisk of accidents, and therefore improve safety. Such information may beof considerable utility, for example, to insurers seeking ways to reducerisk to insured vehicles, whether private or part of a broadercommercial or public fleet.

Other exemplary uses of output data from the analyses of the roadcondition model 190 include generating information for use in travelplanning and in route optimization. The present invention is configuredto extract analyzed and predicted weather conditions out ofhigh-resolution grids, “snapping” out data for any particular linksegment or stretch of road in order to predict how that link or stretchof road is going to respond to weather conditions. This information canthen be utilized to modulate a traffic prediction model in a separateapproach to traffic modeling that would aggregate multiple types of data(pavement surface conditions, traffic, and weather) into, for example, atrip planning tool that provides both estimated travel times andindications of the adversity of travel conditions for various routeoptions and/or departure and arrival times. Such output can further beused to suggest optimal routes and/or departure times so as to minimizeany disturbances, such as the impact of adverse conditions, during atrip.

Accordingly, in another embodiment of the present invention, output datagenerated by the analytical paradigms described herein may be integratedinto a travel planning tool facilitated by one or more the APIs from theplurality of modules described above to provide a plurality of routing,traffic, and weather data. A travel planning tool according to thisembodiment may be configured to provide an informational display ormessage(s) to the user of the tool, including one or more of anindication of road conditions expected along available paths of travelas a function of the user's desired departure or arrival time, andmeasures of the adversity of travel conditions along available paths oftravel as a function of the user's desired departure or arrival timebased on a plurality of traffic, weather, and road conditions, andsuggested paths of travel based on the user's desired departure orarrival time, based on adversity derived from a plurality of traffic,weather, and road conditions.

In addition to generating output of significant value to the generalpublic, there are additional uses of output data from the pavementcondition analysis framework 100 that may include generating informationof quantifiable benefit to the long haul trucking industry. Similar tothe travel planning tool discussed above, the analysis of weatherconditions in high-resolution grids can be applied in a module topredict how a link or stretch of road is going to respond to weatherconditions, and this information can be then be utilized to helptrucking dispatchers manage cargo transit times for safety and/orefficiency, and minimize fuel wasted due to poor traffic flow and/oradverse headwinds. The present invention therefore contemplates that, inanother embodiment, multiple types of data may be modeled and aggregatedinto a dispatch planning tool that integrates roadway condition with oneor more of routing, traffic, and weather data to optimize cargo arrivaland departure times, minimize travel and waiting times, improve safety,and optimize fuel efficiency.

In a further embodiment, output data from the pavement conditionanalysis framework 100 may be integrated with additional real-timetraffic data indicative of traffic abnormalities, which may serve as anindicator that maintenance is needed and as a trigger for arecommendation for winter road maintenance activities based on mobilitypatterns evident in the traffic data. The present invention may furtherbe configured to generate information in the form of push notificationsto public or private agencies when the pavement surface condition ispredicted to have very high pavement temperatures, for example as anindicator of the potential for pavement blow outs.

The present invention also contemplates that output data may begenerated for visual representation of the information containedtherein, for a variety of consumptive uses, such as for distribution tomedia outlets, as noted above. For example, output from the pavementcondition analysis framework 100 may be presented to users as ananimated time-series of cross-sectional profiles of pavement conditions,as well as underlay maps of such conditions, to go along with weatherforecast graphics. Other graphical indicia of road temperatures,conditions, and/or impacts on safety and/or mobility at various timesmay also be presented alongside other weather information (e.g.,television programs have presentations that step through screenshighlighting conditions at varied intervals over a course of time, suchas the next day). The present invention contemplates that roadtemperature and/or condition information may also be incorporated intosuch presentations. In addition to generating content for delivery foruse by media, output data from the road condition model 190 may also bedisplayed by graphical user interfaces via web-based or app-basedmodules. A voice-based output may also be generated to verbally warnmotorists of poor roadway conditions, such as via applications residenton mobile telephony, tablet, or wearable devices.

Accordingly, in yet another embodiment, the present inventioncontemplates integrating output data from the road condition model 190into a broadcast media presentation tool configured to provideinformational displays or messages, either in visual, textual, or audialform, to a broadcast audience. Content of such informational displays ormessages may include one or more of current or expected roadtemperatures, conditions, or other properties, map-based presentationsdepicting the expected response of road conditions to expected weatherevents and traffic influences, cross-sectional profiles depicting theexpected response of pavement conditions to expected weather events andtraffic influences, and indicators of the expected adversity of travelconditions based on the road condition output data.

In still another embodiment, the present invention contemplatesintegrating output data from the road condition model 190 into atraveler-oriented weather or traffic prediction tool or deviceconfigured to provide informational displays or messages, either invisual, textual, or audial form, to one or more users. In thisembodiment, and similar to the content described immediately above,content of such informational displays or messages may include one ormore of current or expected pavement temperatures or conditions,map-based presentations depicting the expected response of pavementconditions to expected weather events and traffic influences,cross-sectional profiles depicting the expected response of pavementconditions to expected weather events and traffic influences, andindicators of the expected adversity of travel conditions.

In yet another embodiment, output data from the pavement conditionanalysis framework 100 may be integrated into one or more toolsconfigured to model logistical planning for commercial and/or onlineretailers, parcel delivery services, and commercial shipping servicessuch as those who move products from location to location to meet retaildemand. The road condition model 190 may be utilized to develop metricsthat enable such services to predict difficulty in making parceldeliveries in the days and weeks leading up to major holidays, such asChristmas. For example, modeling of pavement conditions in response topre-Christmas storms may provide an indication that customers who wouldhave normally made in-store shopping visits would instead me more likelyto order gifts using online sites. The road condition model 190 mayprovide metrics to enable parcel delivery services to anticipate suchevents, which may cause the number of packages that needed to bedelivered to spike.

The pavement condition analysis framework 100 may also be configured tocombine weather and pavement condition metrics as output data that maybe used by retailers to anticipate shopping trends. For example, suchmetrics may indicate a need to shift products from warehouses tolocations expected to be hit by adverse weather, such as items need inwinter or right after a heavy storm. If such metrics indicate that astorm is advancing right before a holiday weekend prior to Christmas,retailers may need to ship products from warehouses to sales locationsin advance of the storm to avoid delays in getting products on storeshelves and meet consumer demand in that short time frame. Similarly,big-box retailers that have experienced sales losses to online retailersmay be interested in ways to keep from losing further business, such asfor example by running marketing campaigns or advertisements thathighlight their own online stores in locations where tough winterweather, travel, or roadway conditions are expected during the holidayshopping season.

It is to be noted that such a logistical planning tool is not to belimited to package delivery services or to commercial shipping servicesduring holiday shopping periods, and that the road condition model 190may therefore also be utilized to improve delivery vehicle routing in awide array of industries. Efficiency improvements for delivery servicesof all types, as well as cost savings and improvements in driver safetyand product spoilage may be realized by the road condition model 190,such as for further example delivery of supermarket stock, shipping ofproducts from ports to distribution warehouses to retail locations,transportation of medicines and time-sensitive supplies for hospitalssuch as blood, etc.

In still another embodiment, output data from the pavement conditionanalysis framework 100 may be configured to provide modeling forpavements of different sizes and shapes, and provide information for alltypes of commuting, such as walking, bicycling, public transit, taxi,and others. For example, one or more tools may be configured toincorporate such output data and generate information for commuters.Where a traveler has indicated or booked a taxi or private liveryservice, the present invention may generate and communicate a messageindicating that the trip may take longer than expected, together with anestimate of how much longer, and why. Similarly, where a pedestrian hasindicated a walking trip from place to place, the present invention maygenerate and communicate a message indicating that unmaintainedsidewalks will be icy, and suggest use of appropriate footwear andcaution, or recommend alternative travel plans. In a publictransportation setting where a commuter that has indicated travel on,for example, a bus, trolley or tram, the present invention may generateand communicate a message indicating that the scheduled bus route islikely to be delayed by x number of minutes due to overnightprecipitation that created icy roads.

The systems and methods of the present invention may be implemented inmany different computing environments. For example, they may beimplemented in conjunction with a special purpose computer, a programmedmicroprocessor or microcontroller and peripheral integrated circuitelement(s), an ASIC or other integrated circuit, a digital signalprocessor, electronic or logic circuitry such as discrete elementcircuit, a programmable logic device or gate array such as a PLD, PLA,FPGA, PAL, and any comparable means. In general, any means ofimplementing the methodology illustrated herein can be used to implementthe various aspects of this invention. Exemplary hardware that can beused for the present invention includes computers, handheld devices,telephones (e.g., cellular, Internet enabled, digital, analog, hybrids,and others), and other such hardware. Some of these devices includeprocessors (e.g., a single or multiple microprocessors), memory,nonvolatile storage, input devices, and output devices. Furthermore,alternative software implementations including, but not limited to,distributed processing, parallel processing, or virtual machineprocessing can also be configured to perform the methods describedherein.

The systems and methods of the present invention may also be partiallyimplemented in software that can be stored on a storage medium, executedon programmed general-purpose computer with the cooperation of acontroller and memory, a special purpose computer, a microprocessor, orthe like. In these instances, the systems and methods of this inventioncan be implemented as a program embedded on personal computer such as anapplet, JAVA® or CGI script, as a resource residing on a server orcomputer workstation, as a routine embedded in a dedicated measurementsystem, system component, or the like. The system can also beimplemented by physically incorporating the system and/or method into asoftware and/or hardware system.

Additionally, the data processing functions disclosed herein may beperformed by one or more program instructions stored in or executed bysuch memory, and further may be performed by one or more modulesconfigured to carry out those program instructions. Modules are intendedto refer to any known or later developed hardware, software, firmware,artificial intelligence, fuzzy logic, expert system or combination ofhardware and software that is capable of performing the data processingfunctionality described herein.

It is to be understood that other embodiments will be utilized andstructural and functional changes will be made without departing fromthe scope of the present invention. The foregoing descriptions ofembodiments of the present invention have been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Accordingly, many modifications and variations are possible in light ofthe above teachings. It is therefore intended that the scope of theinvention be limited not by this detailed description.

The invention claimed is:
 1. A method of integrating traffic, weatherand road condition data for modeling pavement conditions in atransportation infrastructure network, comprising: ingesting, as a firstset of input data, weather data collected from one or more of weathersensors, satellite networks, vehicle-based systems, and numericalweather prediction models, and developing a meteorological profilerepresenting weather conditions on a roadway segment of a transportationinfrastructure for which pavement conditions are to be modeled;ingesting, as a second set of input data, traffic data, and developing atraffic profile representing traffic conditions on the roadway segmentfor which pavement conditions are to be modeled; ingesting, as a thirdset of input data, road condition data collected from one or more ofroad sensors, mobile sensors, and vehicle-based systems, and developingstandardized road condition reports representing roadway conditions onthe roadway segment for which pavement conditions are to be modeled;assimilating, in a computing environment comprised of hardware andsoftware components that include at least one processor configured toanalyze the meteorological profiles, the traffic profiles, and thestandardized road condition reports in a road condition model thatsimulates and iteratively adjusts pavement condition states frombehavior of a pavement response to one or more of 1) changes in statesof moisture resulting from heat and moisture exchanges between apavement surface, the atmosphere, and one or more pavement substrates ina pavement's composition 2) traffic flow characteristics that includedeviations from a normal traffic state at a specified period of timeindicated in the traffic profile, and 3) experienced roadway conditionsfrom the standardized road condition reports, and predicts pavementcondition states over the specified period of time; and generating oneor more of a plurality of output pavement conditions from the simulatedand predicted pavement condition states to output data modulesconfigured to develop spatial, trip, and time-series content in one ormore related application programming interfaces, and interpretations ofthe simulated and predicted pavement condition states for distributionto output data modules configured to develop enhanced commercial contentin one or more related application programming interfaces.
 2. The methodof claim 1, wherein at least one of the weather data, traffic data, androad condition data further includes data collected from crowd-sourcedobservations.
 3. The method of claim 1, wherein the weather sensors arecomprised of at least one of radar systems, surface networks, andimage-based systems.
 4. The method of claim 1, wherein the traffic datais representative of actual traffic conditions that include trafficspeed data, traffic flow data, and incident data, from one or both ofanalyzed traffic characteristics that include traffic informationgenerated by a traffic state estimation platform, and predicted trafficcharacteristics that include predicted traffic speed and predictedtraffic volume generated by a traffic prediction platform.
 5. The methodof claim 1, wherein the road condition model simulates and iterativelyadjusts pavement condition states from behavior of a pavement inresponse to experienced roadway conditions from the standardized roadcondition reports for a specified period of time.
 6. The method of claim1, wherein the road condition data comprises maintenance data relativeto treatments provided to a roadway surface, the vehicle-based systemsincluding systems coupled to winter maintenance vehicles.
 7. The methodof claim 1, further comprising associating the traffic profiles,meteorological profiles, and standardized road condition reports withrepresentative road segment metadata maintained in at least one roadsegment database.
 8. The method of claim 1, wherein the generatinginterpretations of the simulated and predicted pavement condition statesfor distribution to output data modules configured to develop enhancedcommercial content further comprises developing at least one of outputdata packaged for traffic flow models to improve traffic prediction,quantitative metrics of travel difficulty along a specific roadwaysegment that include one or more of textual, visual and numericalsummarizations of pavement condition states in an area including one ormore roadway segments, and one or more of textual, visual, and numericalsummarizations of pavement condition states for a user-defined trip. 9.The method of claim 1, wherein the generating one or more of a pluralityof output pavement conditions from the simulated and predicted pavementcondition states to output data modules further comprises developing atleast one of map tiles and vectorized roadway condition data for amapping function, developing pavement conditions for a set of locationsor times in a trip, and developing pavement conditions at a fixedlocation over time for a time-series display.
 10. The method of claim 1,wherein the generating one or more of a plurality of output pavementconditions from the simulated and predicted pavement conditions statesto output data models further comprises displaying cross-sectionalanimations of a pavement condition on a graphical user interface. 11.The method of claim 1, wherein the generating one or more of a pluralityof output pavement conditions from the simulated and predicted pavementconditions states to output data models further comprises generating adisplay of a pavement condition state in a broadcast media presentationtool configured to provide one or more of visual, textual, an audialcontent to a broadcast audience.
 12. The method of claim 1, furthercomprising ingesting image data received from one or more video camerasconfigured to collect images of the roadway to be modeled, andadjusting, based on one or more visual characteristics within the imagedata, an existing state of the roadway in the road condition model forconsistency with road, traffic and weather conditions indicated in imagedata.
 13. The method of claim 1, wherein predictions of pavementcondition states at least include forecasts of at least one of pavementsurface temperatures and pavement conditions.
 14. A system comprising: acomputer processor; and at least one computer-readable storage mediumoperably coupled to the computer processor and having programinstructions stored therein, the computer processor being operable toexecute the program instructions to operate a road condition model in aplurality of data processing modules, the plurality of data processingmodules including: a data ingest module configured to receive, as inputdata, weather data representing meteorological conditions experienced onsegments of roadway a transportation infrastructure, and traffic datarepresenting traffic conditions experienced on segments of roadway in atransportation infrastructure, and road condition data representingfactors relating to experienced road conditions on segments of atransportation infrastructure; a pavement analysis module configured tosimulate and iteratively adjust pavement condition states from behaviorof a pavement in response to one or more of 1) changes in states ofmoisture resulting from heat and moisture exchanges between the roadsurface, the atmosphere, and one or more pavement substrates in apavement's composition, 2) traffic flow characteristics that includedeviations from a normal traffic state at a specified period of time,and 3) experienced road conditions, and a forecast module configured togenerate predictions of pavement condition states over the specifiedperiod of time.
 15. The system of claim 14, further comprising at leastone module configured to generate content representative of thesimulations and predictions of pavement condition states, the contentconfigured for delivery in at least one of indicia for visual display ona graphical user interface, textual information, or audio, for use bymotorists, for communication to vehicles, for use by industry and publicentities, and as content for distribution to one or more media outlets.16. The system of claim 15, wherein the content further includes thesimulations and predictions of pavement condition states interpreted forat least one of a traffic model application programming interface, amobility application programming interface, an area summary applicationprogramming interface, and trip summary application programminginterface.
 17. The system of claim 15, wherein the content furtherincludes one or more of animated time-series of cross-sectional profilesof the simulations and predictions of pavement conditions states for atime-series data application programming interface, tiles or vectorizedprofiles of pavement conditions configured for display of spatial datain an application programming interface for a mapping function, androadway conditions for a set of locations or times in a trip in anapplication programming interface for display of trip data.
 18. Thesystem of claim 15, wherein the content includes cross-sectionalanimations of a pavement condition for display on a graphical userinterface.
 19. The system of claim 15, wherein the content includes adisplay of a pavement condition state in a broadcast media presentationtool configured to provide one or more of visual, textual, an audialcontent to a broadcast audience.
 20. The system of claim 14, whereindata ingest module is further configured to generate meteorologicalprofiles from the weather data, traffic profiles from the traffic data,and standardized road condition reports from the road condition data.21. The system of claim 14, wherein the data ingest module is furtherconfigured to associate the weather data, the traffic data, and the roadcondition data with road segment network information.
 22. The system ofclaim 14, wherein the ingested traffic data includes actual trafficconditions that include traffic speed data, traffic flow data, andincident data, from one or both of analyzed traffic characteristics thatinclude traffic information generated by a traffic state estimationplatform, and predicted traffic characteristics that include predictedtraffic speed and predicted traffic volume generated by a trafficprediction platform.
 23. A method of modeling pavement conditions in atransportation infrastructure network, comprising: developingassociations of input data to road segment metadata in a plurality ofprofiles in at least one processor in a computing environment comprisedof hardware and software components to enable a plurality of dataprocessing functions in a road condition model, the plurality ofprofiles include a meteorological profile representing weatherconditions, a traffic profile representing traffic conditions, andstandardized road condition reports representing experienced roadconditions, for roadway segments of a transportation infrastructure;simulating and iteratively adjusting pavement condition states from abehavior of a pavement for a roadway segment in response to one or moreof 1) changes in states of moisture resulting from heat and moistureexchanges between the road surface, the atmosphere, and one or morepavement substrates in the pavement's composition as a result of impactson the pavement by weather, traffic, and road conditions experienced onthe roadway segment, 2) traffic flow characteristics that includedeviations from a normal traffic state, at a specified period of time,and 3) the experienced road conditions from the standardized roadcondition reports; and predicting pavement condition states over thespecified period of time, wherein predictions of pavement conditionstates at least comprise forecasts of pavement surface temperatures andconditions.
 24. The method of claim 23, wherein the input data iscomprised of weather data collected from one or more of weather sensors,satellite networks, vehicle-based systems, and numerical weatherprediction models, the weather data representative of one or more ofatmospheric temperature, humidity, wind speed, and precipitation in anarea in which the roadway segments are located.
 25. The method of claim23, wherein the input data is comprised of traffic data representativeof actual conditions in one or both of analyzed traffic characteristicsand predicted traffic characteristics experienced on the roadwaysegments, wherein the actual traffic conditions include traffic speeddata, traffic flow data, and incident data.
 26. The method of claim 25,wherein the analyzed traffic characteristics include traffic informationgenerated by a traffic state estimation platform, and wherein thepredicted traffic characteristics include predicted traffic speed andpredicted traffic volume generated by a traffic prediction platform. 27.The method of claim 23, wherein the input data is comprised of roadcondition data collected from one or more of road sensors, mobilesensors, and vehicle-based systems, the road condition datarepresentative of roadway conditions experienced on the roadwaysegments.
 28. The method of claim 23, further comprising generatingcross-sectional animations of a pavement condition state on a graphicaluser interface.
 29. The method of claim 23, further comprisinggenerating a display of a pavement condition state in a broadcast mediapresentation tool configured to provide one or more of visual, textual,an audial content to a broadcast audience.
 30. The method of claim 23,further comprising generating output data packaged for traffic flowmodels to improve traffic prediction, quantitative metrics of traveldifficulty along a specific roadway segment that include one or more oftextual, visual and numerical summarizations of pavement conditionstates in an area including one or more roadway segments, one or more oftextual, visual, and numerical summarizations of pavement conditionstates for a user-defined trip, and at least one of map tiles andvectorized roadway condition data for a map of one or more roadwaysegments.